Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations16767332
Missing cells33601235
Missing cells (%)16.7%
Duplicate rows9727
Duplicate rows (%)0.1%
Total size in memory1.6 GiB
Average record size in memory104.0 B

Variable types

Numeric8
DateTime1
Categorical2
Text1

Timeseries statistics

Number of series0
Time series length16767332
Starting point2022-07-01 02:00:00
Ending point2023-06-14 10:01:32
Period2.59 seconds
2024-11-02T15:36:25.672659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:36:25.809189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Alerts

Dataset has 9727 (0.1%) duplicate rowsDuplicates
ECGHR has 1856845 (11.1%) missing values Missing
ECGRR has 1591722 (9.5%) missing values Missing
SPO2HR has 5750252 (34.3%) missing values Missing
SPO2 has 5660984 (33.8%) missing values Missing
PI has 6027991 (36.0%) missing values Missing
NIBP_lower has 4230624 (25.2%) missing values Missing
NIBP_upper has 4260455 (25.4%) missing values Missing
NIBP_mean has 4222362 (25.2%) missing values Missing
SPO2 is highly skewed (γ1 = -37.52243112) Skewed
NIBP_lower is highly skewed (γ1 = -23.27630342) Skewed
NIBP_mean is highly skewed (γ1 = -21.41494804) Skewed

Reproduction

Analysis started2024-11-02 15:27:53.280111
Analysis finished2024-11-02 15:36:25.656401
Duration8 minutes and 32.38 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

ECGHR
Real number (ℝ)

Missing 

Distinct347
Distinct (%)< 0.1%
Missing1856845
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean136.11538
Minimum-999
Maximum357
Zeros17
Zeros (%)< 0.1%
Negative233
Negative (%)< 0.1%
Memory size255.8 MiB
2024-11-02T15:36:26.113791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile97
Q1120
median135
Q3151
95-th percentile177
Maximum357
Range1356
Interquartile range (IQR)31

Descriptive statistics

Standard deviation25.852167
Coefficient of variation (CV)0.18992832
Kurtosis60.463797
Mean136.11538
Median Absolute Deviation (MAD)15
Skewness-0.951025
Sum2.0295466 × 109
Variance668.33452
MonotonicityNot monotonic
2024-11-02T15:36:26.456710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 278759
 
1.7%
131 269712
 
1.6%
142 268728
 
1.6%
138 267697
 
1.6%
140 267652
 
1.6%
126 267348
 
1.6%
139 266501
 
1.6%
130 264992
 
1.6%
137 264057
 
1.6%
129 259684
 
1.5%
Other values (337) 12235357
73.0%
(Missing) 1856845
 
11.1%
ValueCountFrequency (%)
-999 233
 
< 0.1%
0 17
 
< 0.1%
13 67
 
< 0.1%
14 73
 
< 0.1%
15 1822
< 0.1%
16 503
 
< 0.1%
17 918
< 0.1%
18 380
 
< 0.1%
19 939
< 0.1%
20 462
 
< 0.1%
ValueCountFrequency (%)
357 2
 
< 0.1%
356 2
 
< 0.1%
355 3
 
< 0.1%
354 2
 
< 0.1%
353 3
 
< 0.1%
352 4
 
< 0.1%
351 4
 
< 0.1%
350 13
< 0.1%
349 2
 
< 0.1%
348 1
 
< 0.1%

ECGRR
Real number (ℝ)

Missing 

Distinct201
Distinct (%)< 0.1%
Missing1591722
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean40.787066
Minimum-999
Maximum200
Zeros113136
Zeros (%)0.7%
Negative96
Negative (%)< 0.1%
Memory size255.8 MiB
2024-11-02T15:36:26.752085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile22
Q131
median39
Q349
95-th percentile68
Maximum200
Range1199
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.787949
Coefficient of variation (CV)0.36256467
Kurtosis156.67056
Mean40.787066
Median Absolute Deviation (MAD)9
Skewness-1.3974051
Sum6.189686 × 108
Variance218.68343
MonotonicityNot monotonic
2024-11-02T15:36:27.061883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 485287
 
2.9%
33 484777
 
2.9%
35 483435
 
2.9%
37 472591
 
2.8%
32 472000
 
2.8%
36 469913
 
2.8%
38 468126
 
2.8%
39 459248
 
2.7%
31 448419
 
2.7%
40 447817
 
2.7%
Other values (191) 10483997
62.5%
(Missing) 1591722
 
9.5%
ValueCountFrequency (%)
-999 96
 
< 0.1%
0 113136
0.7%
2 388
 
< 0.1%
3 487
 
< 0.1%
4 1489
 
< 0.1%
5 2966
 
< 0.1%
6 2533
 
< 0.1%
7 2624
 
< 0.1%
8 3742
 
< 0.1%
9 4851
 
< 0.1%
ValueCountFrequency (%)
200 355
< 0.1%
199 4
 
< 0.1%
198 7
 
< 0.1%
197 1
 
< 0.1%
196 11
 
< 0.1%
195 9
 
< 0.1%
194 46
 
< 0.1%
193 7
 
< 0.1%
192 9
 
< 0.1%
191 6
 
< 0.1%

SPO2HR
Real number (ℝ)

Missing 

Distinct286
Distinct (%)< 0.1%
Missing5750252
Missing (%)34.3%
Infinite0
Infinite (%)0.0%
Mean129.94775
Minimum-999
Maximum300
Zeros0
Zeros (%)0.0%
Negative3701
Negative (%)< 0.1%
Memory size255.8 MiB
2024-11-02T15:36:27.352643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile92
Q1117
median131
Q3145
95-th percentile168
Maximum300
Range1299
Interquartile range (IQR)28

Descriptive statistics

Standard deviation31.34629
Coefficient of variation (CV)0.24122225
Kurtosis563.63941
Mean129.94775
Median Absolute Deviation (MAD)14
Skewness-15.820103
Sum1.4316448 × 109
Variance982.5899
MonotonicityNot monotonic
2024-11-02T15:36:27.640974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 215200
 
1.3%
128 211640
 
1.3%
130 211060
 
1.3%
129 209080
 
1.2%
127 208718
 
1.2%
126 204445
 
1.2%
132 203916
 
1.2%
122 202987
 
1.2%
136 202210
 
1.2%
123 201799
 
1.2%
Other values (276) 8946025
53.4%
(Missing) 5750252
34.3%
ValueCountFrequency (%)
-999 3701
< 0.1%
15 1
 
< 0.1%
16 85
 
< 0.1%
17 334
 
< 0.1%
18 379
 
< 0.1%
19 341
 
< 0.1%
20 425
 
< 0.1%
21 470
 
< 0.1%
22 509
 
< 0.1%
23 445
 
< 0.1%
ValueCountFrequency (%)
300 67
< 0.1%
299 19
 
< 0.1%
298 9
 
< 0.1%
297 10
 
< 0.1%
296 6
 
< 0.1%
295 11
 
< 0.1%
294 9
 
< 0.1%
293 26
 
< 0.1%
292 13
 
< 0.1%
291 23
 
< 0.1%

SPO2
Real number (ℝ)

Missing  Skewed 

Distinct102
Distinct (%)< 0.1%
Missing5660984
Missing (%)33.8%
Infinite0
Infinite (%)0.0%
Mean92.693691
Minimum-999
Maximum100
Zeros45417
Zeros (%)0.3%
Negative3648
Negative (%)< 0.1%
Memory size255.8 MiB
2024-11-02T15:36:27.950767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile78
Q192
median96
Q398
95-th percentile100
Maximum100
Range1099
Interquartile range (IQR)6

Descriptive statistics

Standard deviation22.611219
Coefficient of variation (CV)0.24393482
Kurtosis1783.7374
Mean92.693691
Median Absolute Deviation (MAD)3
Skewness-37.522431
Sum1.0294884 × 109
Variance511.26722
MonotonicityNot monotonic
2024-11-02T15:36:28.236511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 1476253
 
8.8%
98 1238725
 
7.4%
100 1192586
 
7.1%
97 1099585
 
6.6%
96 956212
 
5.7%
95 813581
 
4.9%
94 654771
 
3.9%
93 560681
 
3.3%
92 455445
 
2.7%
91 405452
 
2.4%
Other values (92) 2253057
 
13.4%
(Missing) 5660984
33.8%
ValueCountFrequency (%)
-999 3648
 
< 0.1%
0 45417
0.3%
1 587
 
< 0.1%
2 240
 
< 0.1%
3 1225
 
< 0.1%
4 1274
 
< 0.1%
5 1077
 
< 0.1%
6 1061
 
< 0.1%
7 1265
 
< 0.1%
8 715
 
< 0.1%
ValueCountFrequency (%)
100 1192586
7.1%
99 1476253
8.8%
98 1238725
7.4%
97 1099585
6.6%
96 956212
5.7%
95 813581
4.9%
94 654771
3.9%
93 560681
 
3.3%
92 455445
 
2.7%
91 405452
 
2.4%

PI
Real number (ℝ)

Missing 

Distinct3695
Distinct (%)< 0.1%
Missing6027991
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean3.0068476
Minimum-9.99
Maximum20
Zeros0
Zeros (%)0.0%
Negative3334
Negative (%)< 0.1%
Memory size255.8 MiB
2024-11-02T15:36:28.513649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-9.99
5-th percentile0.25
Q10.9399414
median1.9003906
Q33.5195312
95-th percentile9.203125
Maximum20
Range29.99
Interquartile range (IQR)2.5795898

Descriptive statistics

Standard deviation3.7595925
Coefficient of variation (CV)1.2503436
Kurtosis10.658475
Mean3.0068476
Median Absolute Deviation (MAD)1.140625
Skewness3.0888597
Sum32291561
Variance14.134536
MonotonicityNot monotonic
2024-11-02T15:36:28.826371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 303600
 
1.8%
0.049987793 32706
 
0.2%
0.5800781 32619
 
0.2%
0.05999756 32437
 
0.2%
0.75 32363
 
0.2%
0.6699219 32324
 
0.2%
0.5600586 32296
 
0.2%
0.6201172 32229
 
0.2%
0.5698242 32226
 
0.2%
0.60009766 32194
 
0.2%
Other values (3685) 10144347
60.5%
(Missing) 6027991
36.0%
ValueCountFrequency (%)
-9.99 3334
 
< 0.1%
0.010002136 19
 
< 0.1%
0.02 1
 
< 0.1%
0.020004272 69
 
< 0.1%
0.02999878 2815
 
< 0.1%
0.03 79
 
< 0.1%
0.04 735
 
< 0.1%
0.040008545 22469
0.1%
0.049987793 32706
0.2%
0.05 961
 
< 0.1%
ValueCountFrequency (%)
20 303600
1.8%
19.99 3
 
< 0.1%
19.984375 139
 
< 0.1%
19.98 1
 
< 0.1%
19.97 4
 
< 0.1%
19.96875 70
 
< 0.1%
19.96 1
 
< 0.1%
19.953125 154
 
< 0.1%
19.95 1
 
< 0.1%
19.94 3
 
< 0.1%

NIBP_lower
Real number (ℝ)

Missing  Skewed 

Distinct166
Distinct (%)< 0.1%
Missing4230624
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean60.8551
Minimum-999
Maximum177
Zeros0
Zeros (%)0.0%
Negative8403
Negative (%)0.1%
Memory size255.8 MiB
2024-11-02T15:36:29.117620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile38
Q151
median60
Q370
95-th percentile91
Maximum177
Range1176
Interquartile range (IQR)19

Descriptive statistics

Standard deviation32.391881
Coefficient of variation (CV)0.53227882
Kurtosis765.81399
Mean60.8551
Median Absolute Deviation (MAD)10
Skewness-23.276303
Sum7.6292262 × 108
Variance1049.2339
MonotonicityNot monotonic
2024-11-02T15:36:29.429759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 432420
 
2.6%
63 399816
 
2.4%
61 376868
 
2.2%
57 376220
 
2.2%
58 359869
 
2.1%
51 348555
 
2.1%
55 346264
 
2.1%
54 341094
 
2.0%
64 330196
 
2.0%
56 324765
 
1.9%
Other values (156) 8900641
53.1%
(Missing) 4230624
25.2%
ValueCountFrequency (%)
-999 8403
0.1%
4 2
 
< 0.1%
6 90
 
< 0.1%
7 339
 
< 0.1%
9 369
 
< 0.1%
10 668
 
< 0.1%
11 765
 
< 0.1%
12 4029
< 0.1%
13 1288
 
< 0.1%
14 1413
 
< 0.1%
ValueCountFrequency (%)
177 6943
< 0.1%
175 83
 
< 0.1%
172 172
 
< 0.1%
171 1
 
< 0.1%
170 835
 
< 0.1%
169 302
 
< 0.1%
168 90
 
< 0.1%
166 359
 
< 0.1%
165 276
 
< 0.1%
164 368
 
< 0.1%

NIBP_upper
Real number (ℝ)

Missing 

Distinct209
Distinct (%)< 0.1%
Missing4260455
Missing (%)25.4%
Infinite0
Infinite (%)0.0%
Mean111.82632
Minimum-999
Maximum233
Zeros0
Zeros (%)0.0%
Negative8403
Negative (%)0.1%
Memory size255.8 MiB
2024-11-02T15:36:29.728551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile80
Q199
median110
Q3123
95-th percentile154
Maximum233
Range1232
Interquartile range (IQR)24

Descriptive statistics

Standard deviation36.894742
Coefficient of variation (CV)0.32992895
Kurtosis550.00349
Mean111.82632
Median Absolute Deviation (MAD)12
Skewness-18.070886
Sum1.3985981 × 109
Variance1361.222
MonotonicityNot monotonic
2024-11-02T15:36:30.066684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 377007
 
2.2%
105 330654
 
2.0%
113 319548
 
1.9%
110 307797
 
1.8%
107 301438
 
1.8%
109 296550
 
1.8%
111 290600
 
1.7%
112 290357
 
1.7%
101 285374
 
1.7%
114 274780
 
1.6%
Other values (199) 9432772
56.3%
(Missing) 4260455
25.4%
ValueCountFrequency (%)
-999 8403
0.1%
25 1001
 
< 0.1%
26 215
 
< 0.1%
27 449
 
< 0.1%
28 10
 
< 0.1%
29 10
 
< 0.1%
30 23
 
< 0.1%
32 339
 
< 0.1%
33 712
 
< 0.1%
34 88
 
< 0.1%
ValueCountFrequency (%)
233 353
 
< 0.1%
232 195
 
< 0.1%
231 52
 
< 0.1%
230 99
 
< 0.1%
229 358
 
< 0.1%
228 251
 
< 0.1%
227 305
 
< 0.1%
226 693
 
< 0.1%
225 5519
< 0.1%
224 1050
 
< 0.1%

NIBP_mean
Real number (ℝ)

Missing  Skewed 

Distinct183
Distinct (%)< 0.1%
Missing4222362
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean76.030914
Minimum-999
Maximum199
Zeros0
Zeros (%)0.0%
Negative8403
Negative (%)0.1%
Memory size255.8 MiB
2024-11-02T15:36:30.407261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile52
Q165
median75
Q385
95-th percentile110
Maximum199
Range1198
Interquartile range (IQR)20

Descriptive statistics

Standard deviation33.73329
Coefficient of variation (CV)0.44367861
Kurtosis688.70313
Mean76.030914
Median Absolute Deviation (MAD)10
Skewness-21.414948
Sum9.5380554 × 108
Variance1137.9349
MonotonicityNot monotonic
2024-11-02T15:36:30.708248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 490224
 
2.9%
69 367075
 
2.2%
65 361840
 
2.2%
63 353973
 
2.1%
74 353394
 
2.1%
72 340493
 
2.0%
71 333725
 
2.0%
82 331226
 
2.0%
77 329514
 
2.0%
70 325399
 
1.9%
Other values (173) 8958107
53.4%
(Missing) 4222362
25.2%
ValueCountFrequency (%)
-999 8403
0.1%
6 2
 
< 0.1%
9 339
 
< 0.1%
12 449
 
< 0.1%
13 10
 
< 0.1%
14 1834
 
< 0.1%
15 49
 
< 0.1%
16 1210
 
< 0.1%
17 352
 
< 0.1%
18 633
 
< 0.1%
ValueCountFrequency (%)
199 83
 
< 0.1%
198 416
 
< 0.1%
197 315
 
< 0.1%
191 181
 
< 0.1%
190 618
 
< 0.1%
189 395
 
< 0.1%
187 690
 
< 0.1%
186 443
 
< 0.1%
185 12490
0.1%
184 189
 
< 0.1%
Distinct7557712
Distinct (%)45.1%
Missing0
Missing (%)0.0%
Memory size255.8 MiB
Minimum2022-07-01 02:00:00
Maximum2023-06-14 10:01:32
2024-11-02T15:36:30.978531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:36:31.246736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

monitor_id
Categorical

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size255.8 MiB
IMPALA22050004
1216818 
IMPALA22060028
 
1009842
IMPALA22050003
 
921179
IMPALA22050005
 
917218
IMPALA22060033
 
900942
Other values (25)
11801333 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters234742648
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIMPALA22050002
2nd rowIMPALA22050002
3rd rowIMPALA22050002
4th rowIMPALA22050002
5th rowIMPALA22050002

Common Values

ValueCountFrequency (%)
IMPALA22050004 1216818
 
7.3%
IMPALA22060028 1009842
 
6.0%
IMPALA22050003 921179
 
5.5%
IMPALA22050005 917218
 
5.5%
IMPALA22060033 900942
 
5.4%
IMPALA22060035 868241
 
5.2%
IMPALA22060020 845917
 
5.0%
IMPALA22060026 825777
 
4.9%
IMPALA22060043 718265
 
4.3%
IMPALA22060001 709183
 
4.2%
Other values (20) 7833950
46.7%

Length

2024-11-02T15:36:31.913819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
impala22050004 1216818
 
7.3%
impala22060028 1009842
 
6.0%
impala22050003 921179
 
5.5%
impala22050005 917218
 
5.5%
impala22060033 900942
 
5.4%
impala22060035 868241
 
5.2%
impala22060020 845917
 
5.0%
impala22060026 825777
 
4.9%
impala22060043 718265
 
4.3%
impala22060001 709183
 
4.2%
Other values (20) 7833950
46.7%

Most occurring characters

ValueCountFrequency (%)
0 57447994
24.5%
2 37484351
16.0%
A 33534664
14.3%
I 16767332
 
7.1%
M 16767332
 
7.1%
P 16767332
 
7.1%
L 16767332
 
7.1%
6 15116800
 
6.4%
4 6962827
 
3.0%
3 5918370
 
2.5%
Other values (5) 11208314
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 234742648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 57447994
24.5%
2 37484351
16.0%
A 33534664
14.3%
I 16767332
 
7.1%
M 16767332
 
7.1%
P 16767332
 
7.1%
L 16767332
 
7.1%
6 15116800
 
6.4%
4 6962827
 
3.0%
3 5918370
 
2.5%
Other values (5) 11208314
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 234742648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 57447994
24.5%
2 37484351
16.0%
A 33534664
14.3%
I 16767332
 
7.1%
M 16767332
 
7.1%
P 16767332
 
7.1%
L 16767332
 
7.1%
6 15116800
 
6.4%
4 6962827
 
3.0%
3 5918370
 
2.5%
Other values (5) 11208314
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 234742648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 57447994
24.5%
2 37484351
16.0%
A 33534664
14.3%
I 16767332
 
7.1%
M 16767332
 
7.1%
P 16767332
 
7.1%
L 16767332
 
7.1%
6 15116800
 
6.4%
4 6962827
 
3.0%
3 5918370
 
2.5%
Other values (5) 11208314
 
4.8%
Distinct767
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size255.8 MiB
2024-11-02T15:36:32.268487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters184440652
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB-S-0479386
2nd rowB-S-0479386
3rd rowB-S-0479386
4th rowB-S-0479386
5th rowB-S-0479386
ValueCountFrequency (%)
b-s-3584588 192408
 
1.1%
b-n-6628544 185262
 
1.1%
b-n-5518534 152082
 
0.9%
z-h-6309725 146497
 
0.9%
z-h-6984306 134473
 
0.8%
b-n-8176705 129457
 
0.8%
z-h-8676503 128954
 
0.8%
z-h-2903871 128006
 
0.8%
b-s-7748628 127742
 
0.8%
b-n-7429295 118020
 
0.7%
Other values (757) 15324431
91.4%
2024-11-02T15:36:32.943181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 33534664
18.2%
8 13502687
 
7.3%
4 12339143
 
6.7%
0 12158299
 
6.6%
6 11806003
 
6.4%
5 11797634
 
6.4%
7 11770037
 
6.4%
2 11712666
 
6.4%
3 11512936
 
6.2%
9 10794242
 
5.9%
Other values (6) 43512341
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 184440652
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 33534664
18.2%
8 13502687
 
7.3%
4 12339143
 
6.7%
0 12158299
 
6.6%
6 11806003
 
6.4%
5 11797634
 
6.4%
7 11770037
 
6.4%
2 11712666
 
6.4%
3 11512936
 
6.2%
9 10794242
 
5.9%
Other values (6) 43512341
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 184440652
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 33534664
18.2%
8 13502687
 
7.3%
4 12339143
 
6.7%
0 12158299
 
6.6%
6 11806003
 
6.4%
5 11797634
 
6.4%
7 11770037
 
6.4%
2 11712666
 
6.4%
3 11512936
 
6.2%
9 10794242
 
5.9%
Other values (6) 43512341
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 184440652
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 33534664
18.2%
8 13502687
 
7.3%
4 12339143
 
6.7%
0 12158299
 
6.6%
6 11806003
 
6.4%
5 11797634
 
6.4%
7 11770037
 
6.4%
2 11712666
 
6.4%
3 11512936
 
6.2%
9 10794242
 
5.9%
Other values (6) 43512341
23.6%

location
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size255.8 MiB
zomba
9295260 
blantyre
7472072 

Length

Max length8
Median length5
Mean length6.3368982
Min length5

Characters and Unicode

Total characters106252876
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblantyre
2nd rowblantyre
3rd rowblantyre
4th rowblantyre
5th rowblantyre

Common Values

ValueCountFrequency (%)
zomba 9295260
55.4%
blantyre 7472072
44.6%

Length

2024-11-02T15:36:33.237126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-02T15:36:33.443337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
zomba 9295260
55.4%
blantyre 7472072
44.6%

Most occurring characters

ValueCountFrequency (%)
b 16767332
15.8%
a 16767332
15.8%
z 9295260
8.7%
o 9295260
8.7%
m 9295260
8.7%
l 7472072
7.0%
n 7472072
7.0%
t 7472072
7.0%
y 7472072
7.0%
r 7472072
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106252876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 16767332
15.8%
a 16767332
15.8%
z 9295260
8.7%
o 9295260
8.7%
m 9295260
8.7%
l 7472072
7.0%
n 7472072
7.0%
t 7472072
7.0%
y 7472072
7.0%
r 7472072
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106252876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 16767332
15.8%
a 16767332
15.8%
z 9295260
8.7%
o 9295260
8.7%
m 9295260
8.7%
l 7472072
7.0%
n 7472072
7.0%
t 7472072
7.0%
y 7472072
7.0%
r 7472072
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106252876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 16767332
15.8%
a 16767332
15.8%
z 9295260
8.7%
o 9295260
8.7%
m 9295260
8.7%
l 7472072
7.0%
n 7472072
7.0%
t 7472072
7.0%
y 7472072
7.0%
r 7472072
7.0%

Interactions

2024-11-02T15:34:34.447118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:22.192099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:43.880658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:04.161332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:22.354775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:39.380382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:56.094764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:15.188490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:36.876052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:25.545057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:47.040715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:06.570930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:24.712858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:41.552170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:58.477807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:17.615616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:38.808591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:27.970713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:49.420182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:08.966904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:27.210735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:43.742463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:00.387192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:19.591922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:40.759275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:30.424429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:51.869457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:11.465541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:29.433085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:45.863039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:02.296594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:21.475610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:42.686898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:32.758221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:54.227225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:13.670510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:31.523799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:47.983061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:04.505372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:23.338021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:45.272624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:35.423251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:56.747959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:15.695332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:33.429012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:49.896399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:07.417892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:25.937755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:47.859517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:38.111886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:59.310387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:17.860036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:35.329027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:51.787779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:10.178899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:28.883528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:50.396230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:32:40.727954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:01.828740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:19.962998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:37.194916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:33:53.713138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:12.807740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-02T15:34:31.972846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Missing values

2024-11-02T15:34:53.388397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-02T15:35:12.472672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-02T15:35:54.416286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ECGHRECGRRSPO2HRSPO2PINIBP_lowerNIBP_upperNIBP_meandatetimemonitor_idpatient_idlocation
2022-07-01 02:00:00NaNNaNNaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
2022-07-01 02:00:00120.027.0NaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
2022-07-01 02:00:00NaNNaNNaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
2022-07-01 02:00:00113.025.069.094.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
2022-07-01 02:00:00114.024.0NaN94.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
2022-07-01 02:00:00114.025.065.094.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
2022-07-01 02:00:00116.025.062.098.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
2022-07-01 02:00:00NaNNaNNaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
2022-07-01 02:00:00109.025.062.098.0NaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
2022-07-01 02:00:00NaNNaNNaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre
ECGHRECGRRSPO2HRSPO2PINIBP_lowerNIBP_upperNIBP_meandatetimemonitor_idpatient_idlocation
2023-06-14 10:00:24NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:00:24IMPALA22050003Z-H-7512864zomba
2023-06-14 10:00:29NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:00:29IMPALA22050003Z-H-7512864zomba
2023-06-14 10:00:34NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:00:34IMPALA22050003Z-H-7512864zomba
2023-06-14 10:00:39NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:00:39IMPALA22050003Z-H-7512864zomba
2023-06-14 10:00:44NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:00:44IMPALA22050003Z-H-7512864zomba
2023-06-14 10:00:49NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:00:49IMPALA22050003Z-H-7512864zomba
2023-06-14 10:01:16NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:01:16IMPALA22050003Z-H-7512864zomba
2023-06-14 10:01:21NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:01:21IMPALA22050003Z-H-7512864zomba
2023-06-14 10:01:27NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:01:27IMPALA22050003Z-H-7512864zomba
2023-06-14 10:01:32NaNNaNNaNNaNNaNNaNNaNNaN2023-06-14 10:01:32IMPALA22050003Z-H-7512864zomba

Duplicate rows

Most frequently occurring

ECGHRECGRRSPO2HRSPO2PINIBP_lowerNIBP_upperNIBP_meandatetimemonitor_idpatient_idlocation# duplicates
9670NaNNaNNaNNaNNaNNaNNaNNaN2023-01-25 02:00:00IMPALA22060033B-N-5518534blantyre2765
9672NaNNaNNaNNaNNaNNaNNaNNaN2023-02-02 02:00:00IMPALA22060042B-S-0067250blantyre1673
9385NaNNaNNaNNaNNaN59.0128.076.02023-05-02 02:00:00IMPALA22060005B-N-3318988blantyre1556
9685NaNNaNNaNNaNNaNNaNNaNNaN2023-05-02 02:00:00IMPALA22060005B-N-3318988blantyre1063
9374NaNNaNNaNNaNNaN54.0105.069.02023-03-02 02:00:00IMPALA22060042B-S-6174027blantyre1035
9664NaNNaNNaNNaNNaNNaNNaNNaN2022-07-01 02:00:00IMPALA22050002B-S-0479386blantyre1004
9589NaNNaNNaNNaNNaN70.0110.081.02023-05-02 02:00:00IMPALA22060043B-S-4623049blantyre750
9294NaNNaNNaNNaNNaN49.0109.068.02023-03-05 02:00:00IMPALA22060005B-N-6074430blantyre498
9676NaNNaNNaNNaNNaNNaNNaNNaN2023-03-02 02:00:00IMPALA22060042B-S-6174027blantyre493
9694NaNNaNNaNNaNNaNNaNNaNNaN2023-06-02 02:00:00IMPALA22050004B-S-4882668blantyre448